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*Unverified author*

R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Thu, 06 Nov 2008 15:17:39 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/06/t1226009907uohoe3widnaslua.htm/, Retrieved Sun, 19 May 2024 05:20:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22432, Retrieved Sun, 19 May 2024 05:20:53 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Vermeiren Kevin

Estimated Impact

149

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F     [Notched Boxplots] [workshop 3] [2007-10-26 13:31:48] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F RMPD    [Kendall tau Correlation Matrix] [Hypothesis Testin...] [2008-11-06 22:17:39] [d41d8cd98f00b204e9800998ecf8427e] [Current]
F RMPD      [Star Plot] [Hypothesis Testin...] [2008-11-06 22:32:02] [74be16979710d4c4e7c6647856088456] 
F RMPD        [Notched Boxplots] [Hypothesis Testin...] [2008-11-06 22:45:17] [74be16979710d4c4e7c6647856088456] 

Feedback Forum

2008-11-10 12:26:22 [Lana Van Wesemael] [reply] 
Goed opgelost. Een voordeel van de Kendall Tau Correlation is dat deze met slechts 1 bewerking een overzicht geeft van alle correlaties.  
Om wat vollediger te zijn in je antwoord zou je nog kunnen schrijven dat de p-waarden liefst onder de 0.05 liggen om als betrouwbaar bestempeld te worden. Er zijn nog enekel anderen waarvan de p-waarde onder de 0,05 ligt maar die van de  RCF ligt het dichts bij nul en is bijgevolg dus de meest betrouwbare.   
Ook zou je moeten vermelden dat de gegevens eerst getransponeerd werden voordat je ze verder gebruikte in je verwerking. Zo is het voor anderen eenvoudiger om je berekeningen na te kijken.  

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Dataseries X:

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Histogram

Boxplots

4,2	4,8	20,8	0,9	39,6
2,6	-4,2	17,1	0,85	36,1
3	1,6	22,3	0,83	34,4
3,8	5,2	25,1	0,84	33,4
4	9,2	27,7	0,85	34,8
3,5	4,6	24,9	0,83	33,7
4,1	10,6	29,5	0,83	36,3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22432&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22432&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22432&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Kendall tau rank correlations for all pairs of data series
pair	tau	p-value
tau( RNVM , RNR )	0.714285714285714	0.0301587301587301
tau( RNVM , RCF )	0.523809523809524	0.136111111111111
tau( RNVM , RLEZ )	0.264628062012482	0.427262856745706
tau( RNVM , REV )	0.333333333333333	0.381349206349206
tau( RNR , RCF )	0.80952380952381	0.0107142857142857
tau( RNR , RLEZ )	-0.0529256124024963	0.873844698517373
tau( RNR , REV )	0.0476190476190476	1
tau( RCF , RLEZ )	-0.264628062012482	0.427262856745706
tau( RCF , REV )	-0.142857142857143	0.772619047619048
tau( RLEZ , REV )	0.370479286817474	0.266379923342483

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( RNVM , RNR ) & 0.714285714285714 & 0.0301587301587301 \tabularnewline
tau( RNVM , RCF ) & 0.523809523809524 & 0.136111111111111 \tabularnewline
tau( RNVM , RLEZ ) & 0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RNVM , REV ) & 0.333333333333333 & 0.381349206349206 \tabularnewline
tau( RNR , RCF ) & 0.80952380952381 & 0.0107142857142857 \tabularnewline
tau( RNR , RLEZ ) & -0.0529256124024963 & 0.873844698517373 \tabularnewline
tau( RNR , REV ) & 0.0476190476190476 & 1 \tabularnewline
tau( RCF , RLEZ ) & -0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RCF , REV ) & -0.142857142857143 & 0.772619047619048 \tabularnewline
tau( RLEZ , REV ) & 0.370479286817474 & 0.266379923342483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22432&T=1

[TABLE]
[ROW][C]Kendall tau rank correlations for all pairs of data series[/C][/ROW]
[ROW][C]pair[/C][C]tau[/C][C]p-value[/C][/ROW]
[ROW][C]tau( RNVM , RNR )[/C][C]0.714285714285714[/C][C]0.0301587301587301[/C][/ROW]
[ROW][C]tau( RNVM , RCF )[/C][C]0.523809523809524[/C][C]0.136111111111111[/C][/ROW]
[ROW][C]tau( RNVM , RLEZ )[/C][C]0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( RNVM , REV )[/C][C]0.333333333333333[/C][C]0.381349206349206[/C][/ROW]
[ROW][C]tau( RNR , RCF )[/C][C]0.80952380952381[/C][C]0.0107142857142857[/C][/ROW]
[ROW][C]tau( RNR , RLEZ )[/C][C]-0.0529256124024963[/C][C]0.873844698517373[/C][/ROW]
[ROW][C]tau( RNR , REV )[/C][C]0.0476190476190476[/C][C]1[/C][/ROW]
[ROW][C]tau( RCF , RLEZ )[/C][C]-0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( RCF , REV )[/C][C]-0.142857142857143[/C][C]0.772619047619048[/C][/ROW]
[ROW][C]tau( RLEZ , REV )[/C][C]0.370479286817474[/C][C]0.266379923342483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22432&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22432&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Kendall tau rank correlations for all pairs of data series
pair	tau	p-value
tau( RNVM , RNR )	0.714285714285714	0.0301587301587301
tau( RNVM , RCF )	0.523809523809524	0.136111111111111
tau( RNVM , RLEZ )	0.264628062012482	0.427262856745706
tau( RNVM , REV )	0.333333333333333	0.381349206349206
tau( RNR , RCF )	0.80952380952381	0.0107142857142857
tau( RNR , RLEZ )	-0.0529256124024963	0.873844698517373
tau( RNR , REV )	0.0476190476190476	1
tau( RCF , RLEZ )	-0.264628062012482	0.427262856745706
tau( RCF , REV )	-0.142857142857143	0.772619047619048
tau( RLEZ , REV )	0.370479286817474	0.266379923342483

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method='kendall')
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Kendall tau rank correlations for all pairs of data series',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'tau',1,TRUE)
a<-table.element(a,'p-value',1,TRUE)
a<-table.row.end(a)
n <- length(y[,1])
n
cor.test(y[1,],y[2,],method='kendall')
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste('tau(',dimnames(t(x))[[2]][i])
dum <- paste(dum,',')
dum <- paste(dum,dimnames(t(x))[[2]][j])
dum <- paste(dum,')')
a<-table.element(a,dum,header=TRUE)
r <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,r$estimate)
a<-table.element(a,r$p.value)
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable.tab')

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